In Journal of magnetic resonance imaging : JMRI
BACKGROUND : Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast-weighted images, where pathology is detected once it reached a certain visual threshold. Computer-aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology.
PURPOSE : To compare conventional (i.e., visual) MRI assessment of artificially generated multiple sclerosis (MS) lesions in the brain's white matter to CAD based on a deep neural network.
STUDY TYPE : Prospective.
POPULATION : A total of 25 neuroradiologists (15 males, age 39 ± 9, 9 ± 9.8 years of experience) independently assessed all synthetic lesions.
FIELD STRENGTH/SEQUENCE : A 3.0 T, T2 -weighted multi-echo spin-echo (MESE) sequence.
ASSESSMENT : MS lesions of varying severity levels were artificially generated in healthy volunteer MRI scans by manipulating T2 values. Radiologists and a neural network were tasked with detecting these lesions in a series of 48 MR images. Sixteen images presented healthy anatomy and the rest contained a single lesion at eight increasing severity levels (6%, 9%, 12%, 15%, 18%, 21%, 25%, and 30% elevation in T2 ). True positive (TP) rates, false positive (FP) rates, and odds ratios (ORs) were compared between radiological diagnosis and CAD across the range lesion severity levels.
STATISTICAL TESTS : Diagnostic performance of the two approaches was compared using z-tests on TP rates, FP rates, and the logarithm of ORs across severity levels. A P-value <0.05 was considered statistically significant.
RESULTS : ORs of identifying pathology were significantly higher for CAD vis-à-vis visual inspection for all lesions' severity levels. For a 6% change in T2 value (lowest severity), radiologists' TP and FP rates were not significantly different (P = 0.12), while the corresponding CAD results remained statistically significant.
DATA CONCLUSION : CAD is capable of detecting the presence or absence of more subtle lesions with greater precision than the representative group of 25 radiologists chosen in this study.
LEVEL OF EVIDENCE : 1 TECHNICAL EFFICACY: Stage 3.
Solomon Chen, Shmueli Omer, Shrot Shai, Blumenfeld-Katzir Tamar, Radunsky Dvir, Omer Noam, Stern Neta, Reichman Dominique Ben-Ami, Hoffmann Chen, Salti Moti, Greenspan Hayit, Ben-Eliezer Noam
2022-Dec-09
computer-aided diagnosis, deep learning, multiple sclerosis, psychophysics
